Int J Mol Sci. 2024 Dec 30;26(1):260. doi: 10.3390/ijms26010260.

ABSTRACT

Statins are the primary drugs used to prevent cardiovascular disease by inhibiting the HMG-CoA reductase, an enzyme crucial for the synthesis of LDL cholesterol in the liver. A significant number of patients experience adverse drug reactions (ADRs), particularly musculoskeletal problems, which can affect adherence to treatment. Recent clinical guidelines, such as those from the Clinical Pharmacogenetics Implementation Consortium (CPIC) in 2022, recommend adjusting rosuvastatin doses based on genetic variations in the ABCG2 and SLCO1B1 genes to minimize ADRs and improve treatment efficacy. Despite these adjustments, some patients still experience ADRs. So, we performed a candidate gene study to better understand the pharmacogenetics of rosuvastatin. This study included 119 healthy volunteers who participated in three bioequivalence trials of rosuvastatin alone or in combination with ezetimibe at the Clinical Trials Unit of the Hospital Universitario de La Princesa (UECHUP). Participants were genotyped using a custom OpenArray from ThermoFisher that assessed 124 variants in 38 genes associated with drug metabolism and transport. No significant differences were observed according to sex or biogeographic origin. A significant increase in t1/2 (pmultivariate(pmv) = 0.013) was observed in the rosuvastatin plus ezetimibe trial compared with the rosuvastatin alone trials. Genetic analysis showed that decreased (DF) and poor function (PF) volunteers for the ABCG2 transporter had higher AUC∞/DW (adjusted dose/weight), AUC72h/DW and Cmax/DW compared to normal function (NF) volunteers (pmv< 0.001). DF and PF volunteers for SLCO1B1 showed an increase in AUC72h/DW (pmv = 0.020) compared to increased (IF) and NF individuals. Results for ABCG2 and SLCO1B1 were consistent with the existing literature. In addition, AUC∞/DW, AUC72h/DW and Cmax/DW were increased in intermediate (IA) and poor (PA) NAT2 acetylators (pmv = 0.001, pmv< 0.001, pmv< 0.001, respectively) compared to rapid acetylators (RA), which could be associated through a secondary pathway that was previously unknown.

PMID:39796117 | DOI:10.3390/ijms26010260

Int J Mol Sci. 2024 Dec 26;26(1):103. doi: 10.3390/ijms26010103.

ABSTRACT

Asthma is a common complex disease with susceptibility defined through an interplay of genetic and environmental factors. Responsiveness to asthma treatment varies between individuals and is largely determined by genetic variability. The polygenic score (PGS) approach enables an individual risk of asthma and respective response to drug therapy. PGS models could help to predict the individual risk of asthma using 26 SNPs of drug pathway genes involved in the metabolism of glucocorticosteroids (GCS), and beta-2-agonists, antihistamines, and antileukotriene drugs associated with the response to asthma treatment within GWAS were built. For PGS, summary statistics from the Trans-National Asthma Genetic Consortium GWAS meta-analysis, and genotype data for 882 individuals with asthma/controls from the Volga-Ural region, were used. The study group was comprised of Russian, Tatar, Bashkir, and mixed ethnicity individuals with asthma (N = 378) aged 2-18 years. and individuals without features of atopic disease (N = 504) aged 4-67 years from the Volga-Ural region. The DNA samples for the study were collected from 2000 to 2021. The drug pathway genes' PGS revealed a higher odds for childhood asthma risk (p = 2.41 × 10-12). The receiver operating characteristic (ROC) analysis showed an Area Under the Curve, AUC = 0.63. The AUC of average significance for moderate-to-severe and severe asthma was observed (p = 5.7 × 10-9, AUC = 0.64). Asthma drug response pathway gene variant PGS models may contribute to the development of modern approaches to optimise asthma diagnostics and treatment.

PMID:39795959 | DOI:10.3390/ijms26010103

Glycoconj J. 2025 Jan 11. doi: 10.1007/s10719-024-10174-7. Online ahead of print.

ABSTRACT

Cystic Fibrosis (CF) is a life-threatening hereditary disease resulting from mutations in the Cystic Fibrosis Transmembrane Conductance Regulator (CFTR) gene that encodes a chloride channel essential for ion transport in epithelial cells. Mutations in CFTR, notably the prevalent F508del mutation, impair chloride transport, severely affecting the respiratory system and leading to recurrent infections. Recent therapeutic advancements include CFTR modulators such as ETI, a combination of two correctors (Elexacaftor and Tezacaftor) and a potentiator (Ivacaftor), that can improve CFTR function in patients with the F508del mutation. This study investigated ETI's impact on the maturation of the mutated CFTR, the expression levels of its scaffolding proteins, and lipid composition of cells using bronchial epithelial cell lines expressing both wild-type and F508del CFTR. Our findings revealed that ETI treatment enhances CFTR and its scaffolding proteins expression and aids in rescuing mature F508del CFTR, causing also significant alterations in the lipid profile including reduced levels of lactosylceramide and increased content of gangliosides GM1 and GD1a. These changes were linked to ETI's influence on enzymes involved in the sphingolipid metabolism, in particular GM3 synthase and sialidase. Through this work, we aim to deepen understanding CFTR interactions with lipids, and to elucidate the mechanisms of action of CFTR modulators. Our findings may support the development of potential therapeutic strategies contributing to the ongoing efforts to design effective correctors and potentiators for CF treatment.

PMID:39797966 | DOI:10.1007/s10719-024-10174-7

J Paediatr Child Health. 2025 Jan 11. doi: 10.1111/jpc.16773. Online ahead of print.

ABSTRACT

BACKGROUND: Children with cystic fibrosis are more likely to become severely unwell with influenza-associated illness compared to children without chronic lung disease. The provision of accessible influenza vaccinations is essential in the prevention of infection.

OBJECTIVES: To describe the prevalence of the influenza vaccine uptake in children with cystic fibrosis from 2016 to 2020 at a single tertiary paediatric hospital site and determine if the COVID pandemic of 2020 and the introduction of telehealth encounters affected the vaccine uptake.

METHODS: A retrospective study of children with cystic fibrosis aged 6 months to 18 years who reside in South Australia was performed using the Women's and Children's Hospital (WCH) Respiratory Department cystic fibrosis database from 2016 to 2020. The Australian Immunisation Register (AIR) was used to determine vaccination status during this period.

RESULTS: One hundred and eighty two children with cystic fibrosis were identified, one hundred and seventy two of whom vaccination records were available on the Australian Immunisation Register. Proportion of eligible patients who were vaccinated ranged from 66% to 88% in the years 2016-2019. There was no significant decrease in uptake during the COVID-19 pandemic in 2020 (75%). Despite the introduction of Telehealth reviews in 2020 majority (66%) of patients continued to have exclusive face-to-face appointments, of which 73% received the vaccination. Vaccination coverage of those who received combination of Telehealth and face-to-face was 81%.

CONCLUSION: The high influenza vaccination rate of South Australian children with cystic fibrosis is consistent with rates seen in other tertiary centres globally. This study demonstrated that the pandemic and introduction of Telehealth appointments did not have any impact in the uptake of the influenza vaccination in our South Australian population.

PMID:39797528 | DOI:10.1111/jpc.16773

Mol Ther. 2025 Jan 9:S1525-0016(25)00015-2. doi: 10.1016/j.ymthe.2025.01.011. Online ahead of print.

ABSTRACT

Cystic Fibrosis (CF) is a life-shortening autosomal recessive disease caused by mutations in the CFTR gene, resulting in functional impairment of the encoded ion channel. F508del mutation, a trinucleotide deletion, is the most frequent cause of CF affecting approximately 80% of persons with cystic fibrosis (pwCFs). Even though current pharmacological treatments alleviate the F508del-CF disease symptoms there is no definitive cure. Here we leveraged revertant mutations (RMs) in cis with F508del to rescue CFTR protein folding and restore its function. We developed CRISPR base editing strategies to efficiently and precisely introduce the desired mutations in the F508del locus. Both editing and CFTR function recovery were verified in CF cellular models including primary epithelial cells derived from pwCFs. The efficacy of the CFTR recovery strategy was validated in cultures of pseudostratified epithelia from pwCF cells showing full recovery of ion transport. Additionally, we observed an additive effect by combining our strategy with small molecules that enhance F508del activity, thus paving the way to combinatorial therapies.

PMID:39797401 | DOI:10.1016/j.ymthe.2025.01.011

Int J Mol Sci. 2025 Jan 6;26(1):425. doi: 10.3390/ijms26010425.

ABSTRACT

Granulomatous mastitis (GM) is a rare, benign, but chronic and recurrent inflammatory breast disease that significantly impacts physical and psychological well-being. It often presents symptoms such as pain, swelling, and discharge, leading to diagnostic confusion with malignancy. The etiology of GM remains unclear, though autoimmune and multifactorial components are suspected. This study aimed to explore the genetic underpinnings of GM using whole-exome sequencing (WES) on 22 GM patients and 52 healthy controls to identify single nucleotide variants (SNVs) and copy number variations (CNVs) potentially linked to the disease. WES analysis revealed novel SNVs in six genes: BRCA2 (rs169547), CFTR (rs4727853), NCF1 (rs10614), PTPN22 (rs2476601), HLA-DRB1 (seven variants), and C3 (rs406514). Notably, most of these variants are associated with immune regulation and inflammatory pathways, supporting the hypothesis that GM is an autoimmune disease. However, all identified variants were classified as benign according to the American College of Medical Genetics and Genomics (ACMG) guidelines, necessitating further investigation into their potential functional effects. Despite conducting CNV analysis, no significant variations were identified. This study represents a foundational step in linking genetic predisposition to GM and highlights the need for integrating genetic, clinical, and functional data to better understand GM's pathophysiology. Future research should focus on larger cohorts, functional studies, and exploring multifactorial contributors to GM, including hormonal and environmental factors.

PMID:39796280 | DOI:10.3390/ijms26010425

Diagnostics (Basel). 2025 Jan 4;15(1):107. doi: 10.3390/diagnostics15010107.

ABSTRACT

Objectives: To determine the association between airflow limitation and the quantification of lung attenuation in computed tomography (CT) in adult patients with cystic fibrosis (CF). Methods: A cross-sectional study in a single center between January 2013 and December 2018 in adult patients with stable CF. We collected clinical data and the results of spirometry and plethysmography. A chest CT at inspiration and expiration, using a specific software that automatically measured the lung attenuation, was performed. Results: In total, 73 patients (63% males) were included. The mean age was 31.6 ± 12.3 years and the FEV1 was 67.8 ± 25.9% pred. An airflow limitation was found in 63%, the mean residual volume was 159.9% pred, and air trapping was observed in 50 (87.7%) of the patients. The patients with airflow limitations showed a higher bulla index and a percentage of lung voxels in the range of emphysema. The FEV1 and the FEV1/FVC correlated with the percentage of the lungs at a high attenuation value (HAV), the range of emphysema, and the bulla index at inspiration, as well as the mean lung density at expiration and the inspiratory-expiratory variation of the mean lung density (MLDi-e). Finally, in the multivariate model, the MLDi-e and the HAV at inspiration were associated with airflow limitations. Conclusions: The measurements obtained from the automated quantification of lung parenchymal attenuation predicts airflow limitation in CF.

PMID:39795635 | DOI:10.3390/diagnostics15010107

MAGMA. 2025 Jan 11. doi: 10.1007/s10334-024-01221-3. Online ahead of print.

ABSTRACT

OBJECTIVE: Segmentation of individual thigh muscles in MRI images is essential for monitoring neuromuscular diseases and quantifying relevant biomarkers such as fat fraction (FF). Deep learning approaches such as U-Net have demonstrated effectiveness in this field. However, the impact of reducing neural network complexity remains unexplored in the FF quantification in individual muscles.

MATERIAL AND METHODS: U-Net architectures with different complexities have been compared for the quantification of the fat fraction in each muscle group selected in the central part of the thigh region. The corresponding performance has been assessed in terms of Dice score (DSC) and FF quantification error. The database contained 1450 thigh images from 59 patients and 14 healthy subjects (age: 47 ± 17 years, sex: 36F, 37M). Ten individual muscles were segmented in each image. The performance of each model was compared to nnU-Net, a complex architecture with 4.35 × 107 parameters, 12.8 Gigabytes of peak memory usage and 167 h of training time.

RESULTS: As expected, nnU-Net achieved the highest DSC (94.77 ± 0.13%). A simpler U-Net (5.81 × 105 parameters, 2.37 Gigabytes, 14 h of training time) achieved a lower DSC but still above 90%. Surprisingly, both models achieved a comparable FF estimation.

DISCUSSION: The poor correlation between observed DSC and FF indicates that less complex architectures, reducing GPU memory utilization and training time, can still accurately quantify FF.

PMID:39798067 | DOI:10.1007/s10334-024-01221-3

Acta Otolaryngol. 2025 Jan 10:1-6. doi: 10.1080/00016489.2024.2448829. Online ahead of print.

ABSTRACT

BACKGROUND: Recent advances in artificial intelligence have facilitated the automatic diagnosis of middle ear diseases using endoscopic tympanic membrane imaging.

AIM: We aimed to develop an automated diagnostic system for middle ear diseases by applying deep learning techniques to tympanic membrane images obtained during routine clinical practice.

MATERIAL AND METHODS: To augment the training dataset, we explored the use of generative adversarial networks (GANs) to produce high-quality synthetic tympanic images that were subsequently added to the training data. Between 2016 and 2021, we collected 472 endoscopic images representing four tympanic membrane conditions: normal, acute otitis media, otitis media with effusion, and chronic suppurative otitis media. These images were utilized for machine learning based on the InceptionV3 model, which was pretrained on ImageNet. Additionally, 200 synthetic images generated using StyleGAN3 and considered appropriate for each disease category were incorporated for retraining.

RESULTS: The inclusion of synthetic images alongside real endoscopic images did not significantly improve the diagnostic accuracy compared to training solely with real images. However, when trained solely on synthetic images, the model achieved a diagnostic accuracy of approximately 70%.

CONCLUSIONS AND SIGNIFICANCE: Synthetic images generated by GANs have potential utility in the development of machine-learning models for medical diagnosis.

PMID:39797517 | DOI:10.1080/00016489.2024.2448829

Clin Transl Sci. 2025 Jan;18(1):e70124. doi: 10.1111/cts.70124.

ABSTRACT

The pharmaceutical industry constantly strives to improve drug development processes to reduce costs, increase efficiencies, and enhance therapeutic outcomes for patients. Model-Informed Drug Development (MIDD) uses mathematical models to simulate intricate processes involved in drug absorption, distribution, metabolism, and excretion, as well as pharmacokinetics and pharmacodynamics. Artificial intelligence (AI), encompassing techniques such as machine learning, deep learning, and Generative AI, offers powerful tools and algorithms to efficiently identify meaningful patterns, correlations, and drug-target interactions from big data, enabling more accurate predictions and novel hypothesis generation. The union of MIDD with AI enables pharmaceutical researchers to optimize drug candidate selection, dosage regimens, and treatment strategies through virtual trials to help derisk drug candidates. However, several challenges, including the availability of relevant, labeled, high-quality datasets, data privacy concerns, model interpretability, and algorithmic bias, must be carefully managed. Standardization of model architectures, data formats, and validation processes is imperative to ensure reliable and reproducible results. Moreover, regulatory agencies have recognized the need to adapt their guidelines to evaluate recommendations from AI-enhanced MIDD methods. In conclusion, integrating model-driven drug development with AI offers a transformative paradigm for pharmaceutical innovation. By integrating the predictive power of computational models and the data-driven insights of AI, the synergy between these approaches has the potential to accelerate drug discovery, optimize treatment strategies, and usher in a new era of personalized medicine, benefiting patients, researchers, and the pharmaceutical industry as a whole.

PMID:39797502 | DOI:10.1111/cts.70124

Small Methods. 2025 Jan 10:e2401757. doi: 10.1002/smtd.202401757. Online ahead of print.

ABSTRACT

The integration of Machine Learning (ML) with super-resolution microscopy represents a transformative advancement in biomedical research. Recent advances in ML, particularly deep learning (DL), have significantly enhanced image processing tasks, such as denoising and reconstruction. This review explores the growing potential of automation in super-resolution microscopy, focusing on how DL can enable autonomous imaging tasks. Overcoming the challenges of automation, particularly in adapting to dynamic biological processes and minimizing manual intervention, is crucial for the future of microscopy. Whilst still in its infancy, automation in super-resolution can revolutionize drug discovery and disease phenotyping leading to similar breakthroughs as have been recognized in this year's Nobel Prizes for Physics and Chemistry.

PMID:39797467 | DOI:10.1002/smtd.202401757

J Clin Med. 2025 Jan 6;14(1):271. doi: 10.3390/jcm14010271.

ABSTRACT

Background: Accurate segmentation of the left ventricular myocardium in cardiac MRI is essential for developing reliable deep learning models to diagnose left ventricular non-compaction cardiomyopathy (LVNC). This work focuses on improving the segmentation database used to train these models, enhancing the quality of myocardial segmentation for more precise model training. Methods: We present a semi-automatic framework that refines segmentations through three fundamental approaches: (1) combining neural network outputs with expert-driven corrections, (2) implementing a blob-selection method to correct segmentation errors and neural network hallucinations, and (3) employing a cross-validation process using the baseline U-Net model. Results: Applied to datasets from three hospitals, these methods demonstrate improved segmentation accuracy, with the blob-selection technique boosting the Dice coefficient for the Trabecular Zone by up to 0.06 in certain populations. Conclusions: Our approach enhances the dataset's quality, providing a more robust foundation for future LVNC diagnostic models.

PMID:39797353 | DOI:10.3390/jcm14010271

Sensors (Basel). 2025 Jan 6;25(1):291. doi: 10.3390/s25010291.

ABSTRACT

Bird species detection is critical for applications such as the analysis of bird population dynamics and species diversity. However, this task remains challenging due to local structural similarities and class imbalances among bird species. Currently, most deep learning algorithms focus on designing local feature extraction modules while ignoring the importance of global information. However, this global information is essential for accurate bird species detection. To address this limitation, we propose BSD-Net, a bird species detection network. BSD-Net efficiently learns local and global information in pixels to accurately detect bird species. BSD-Net consists of two main components: a dual-branch feature mixer (DBFM) and a prediction balancing module (PBM). The dual-branch feature mixer extracts features from dichotomous feature segments using global attention and deep convolution, expanding the network's receptive field and achieving a strong inductive bias, allowing the network to distinguish between similar local details. The prediction balance module balances the difference in feature space based on the pixel values of each category, thereby resolving category imbalances and improving the network's detection accuracy. The experimental results using two public benchmarks and a self-constructed Poyang Lake Bird dataset demonstrate that BSD-Net outperforms existing methods, achieving 45.71% and 80.00% mAP50 with the CUB-200-2011 and Poyang Lake Bird datasets, respectively, and 66.03% AP with FBD-SV-2024, allowing for more accurate location and species information for bird detection tasks in video surveillance.

PMID:39797082 | DOI:10.3390/s25010291

Sensors (Basel). 2025 Jan 6;25(1):287. doi: 10.3390/s25010287.

ABSTRACT

Soil colour is a key indicator of soil health and the associated properties. In agriculture, soil colour provides farmers and advises with a visual guide to interpret soil functions and performance. Munsell colour charts have been used to determine soil colour for many years, but the process is fallible, as it depends on the user's perception. As smartphones are widely used and come with high-quality cameras, a popular one was used for capturing images for this study. This study aims to predict Munsell soil colour (MSC) from the Munsell soil colour book (MSCB) by using deep learning techniques on mobile-captured images. MSCB contains 14 pages and 443 colour chips. So, the number of classes for chip-by-chip prediction is very high, and the captured images are inadequate to train and validate using deep learning methods; thus, a patch-based mechanism was proposed to enrich the dataset. So, the course of action is to find the prediction accuracy of MSC for both page level and chip level by evaluating multiple deep learning methods combined with a patch-based mechanism. The analysis also provides knowledge about the best deep learning technique for MSC prediction. Without patching, the accuracy for chip-level prediction is below 40%, the page-level prediction is below 65%, and the accuracy with patching is around 95% for both, which is significant. Lastly, this study provides insights into the application of the proposed techniques and analysis within real-world soil and provides results with higher accuracy with a limited number of soil samples, indicating the proposed method's potential scalability and effectiveness with larger datasets.

PMID:39797078 | DOI:10.3390/s25010287

Sensors (Basel). 2025 Jan 6;25(1):273. doi: 10.3390/s25010273.

ABSTRACT

The Loess Plateau in northwest China features fragmented terrain and is prone to landslides. However, the complex environment of the Loess Plateau, combined with the inherent limitations of convolutional neural networks (CNNs), often results in false positives and missed detection for deep learning models based on CNNs when identifying landslides from high-resolution remote sensing images. To deal with this challenge, our research introduced a CNN-transformer hybrid network. Specifically, we first constructed a database consisting of 1500 loess landslides and non-landslide samples. Subsequently, we proposed a neural network architecture that employs a CNN-transformer hybrid as an encoder, with the ability to extract high-dimensional, local-scale features using CNNs and global-scale features using a multi-scale lightweight transformer module, thereby enabling the automatic identification of landslides. The results demonstrate that this model can effectively detect loess landslides in such complex environments. Compared to approaches based on CNNs or transformers, such as U-Net, HCNet and TransUNet, our proposed model achieved greater accuracy, with an improvement of at least 3.81% in the F1-score. This study contributes to the automatic and intelligent identification of landslide locations and ranges on the Loess Plateau, which has significant practicality in terms of landslide investigation, risk assessment, disaster management, and related fields.

PMID:39797065 | DOI:10.3390/s25010273

Sensors (Basel). 2025 Jan 6;25(1):271. doi: 10.3390/s25010271.

ABSTRACT

Person identification is a critical task in applications such as security and surveillance, requiring reliable systems that perform robustly under diverse conditions. This study evaluates the Vision Transformer (ViT) and ResNet34 models across three modalities-RGB, thermal, and depth-using datasets collected with infrared array sensors and LiDAR sensors in controlled scenarios and varying resolutions (16 × 12 to 640 × 480) to explore their effectiveness in person identification. Preprocessing techniques, including YOLO-based cropping, were employed to improve subject isolation. Results show a similar identification performance between the three modalities, in particular in high resolution (i.e., 640 × 480), with RGB image classification reaching 100.0%, depth images reaching 99.54% and thermal images reaching 97.93%. However, upon deeper investigation, thermal images show more robustness and generalizability by maintaining focus on subject-specific features even at low resolutions. In contrast, RGB data performs well at high resolutions but exhibits reliance on background features as resolution decreases. Depth data shows significant degradation at lower resolutions, suffering from scattered attention and artifacts. These findings highlight the importance of modality selection, with thermal imaging emerging as the most reliable. Future work will explore multi-modal integration, advanced preprocessing, and hybrid architectures to enhance model adaptability and address current limitations. This study highlights the potential of thermal imaging and the need for modality-specific strategies in designing robust person identification systems.

PMID:39797062 | DOI:10.3390/s25010271

Sensors (Basel). 2025 Jan 6;25(1):270. doi: 10.3390/s25010270.

ABSTRACT

This study proposes an advanced plant disease classification framework leveraging the Attention Score-Based Multi-Vision Transformer (Multi-ViT) model. The framework introduces a novel attention mechanism to dynamically prioritize relevant features from multiple leaf images, overcoming the limitations of single-leaf-based diagnoses. Building on the Vision Transformer (ViT) architecture, the Multi-ViT model aggregates diverse feature representations by combining outputs from multiple ViTs, each capturing unique visual patterns. This approach allows for a holistic analysis of spatially distributed symptoms, crucial for accurately diagnosing diseases in trees. Extensive experiments conducted on apple, grape, and tomato leaf disease datasets demonstrate the model's superior performance, achieving over 99% accuracy and significantly improving F1 scores compared to traditional methods such as ResNet, VGG, and MobileNet. These findings underscore the effectiveness of the proposed model for precise and reliable plant disease classification.

PMID:39797061 | DOI:10.3390/s25010270

J Clin Med. 2025 Jan 6;14(1):291. doi: 10.3390/jcm14010291.

ABSTRACT

Chronic cough is a distressing and prevalent symptom in interstitial lung disease (ILD), significantly impairing quality of life (QoL) and contributing to disease progression, particularly in idiopathic pulmonary fibrosis (IPF). It is associated with physical discomfort, psychological distress, and social isolation and is often refractory to conventional therapies. The pathophysiology of cough in ILD is complex and multifactorial, involving neural hypersensitivity, structural lung changes, inflammatory processes, and comorbid conditions such as gastroesophageal reflux disease (GERD). Evaluating cough in ILD relies on subjective and objective tools to measure its severity, frequency, and impact on daily life, although standardization of these measures remains challenging. Management strategies span pharmacological interventions, including neuromodulators such as opiates, antifibrotic agents, pharmacologic and surgical GERD treatments, and non-pharmacological approaches like behavioral therapies, cough suppression techniques, and pulmonary rehabilitation and physiotherapy. Emerging treatments, such as P2X3 receptor antagonists and airway hydration therapies, offer promising avenues but require further investigation through robust clinical trials. This review aims to demonstrate the importance of addressing cough in ILD as a significant symptom and present objective and subjective methods of quantifying coughs, while providing insights into effective and emerging therapeutic options. By highlighting these potential therapies, we hope to guide healthcare practitioners in considering them through a thorough evaluation of benefits and risks on a case-by-case basis, with relevance both in the U.S. and internationally.

PMID:39797373 | DOI:10.3390/jcm14010291

J Clin Med. 2025 Jan 3;14(1):247. doi: 10.3390/jcm14010247.

ABSTRACT

Background: Antibodies against Ku have been described in patients with various connective tissue diseases. The objective of this study was to describe the clinical, functional, and imaging characteristics of interstitial lung disease in patients with anti-Ku antibodies. Methods: This single-center, retrospective observational study was conducted at a tertiary referral institution. Patients with positive anti-Ku antibodies and interstitial lung disease identified between 2007 and 2022 were included. Clinical, immunological, functional, and imaging data were systematically reviewed. Results: Nineteen patients (ten females) with a mean age of 59 ± 12.6 years were included. The most frequent associated diagnosis was systemic sclerosis (42%), followed by rheumatoid arthritis (26%), Sjögren syndrome, undifferentiated connective tissue disease, and overlap between systemic sclerosis and idiopathic inflammatory myopathy (scleromyositis). Imaging revealed frequent septal and intralobular reticulations and ground-glass opacities, with nonspecific interstitial pneumonia as the predominant pattern (53%). The mean forced vital capacity was 82% ± 26 of the predicted value, and the mean diffusing capacity for carbon monoxide was 55% ± 21. Over the first year of follow-up, the mean annual forced vital capacity decline was 140 mL/year (range: 0-1610 mL/year). The overall survival rate was 82% at 5 years and 67% at 10 years. Conclusions: Most patients with interstitial lung disease and anti-Ku antibodies presented with dyspnea, a mild-to-moderate restrictive ventilatory pattern, and reduced diffusing capacity for carbon monoxide. The CT pattern was heterogeneous but was consistent with nonspecific interstitial pneumonia in half of the patients.

PMID:39797328 | DOI:10.3390/jcm14010247

Int J Mol Sci. 2025 Jan 4;26(1):382. doi: 10.3390/ijms26010382.

ABSTRACT

Idiopathic pulmonary fibrosis (IPF) is a chronic, progressive, and devastating lung disorder. In response to transforming growth factor-β (TGF-β), normal lung cells proliferate and differentiate into myofibroblasts, which are instrumental in promoting disease progression. Small interfering RNA (siRNA) targeting heat shock protein 47 (HSP47) has been demonstrated to alleviate IPF by blocking collagen synthesis and secretion. Exosomes (EXOs) have been investigated for drug delivery due to their superior carrier properties. However, their loading efficiency has been a limiting factor in widely application as drug carriers. In this study, an ultrasonic microfluidic method was employed to enhance the loading efficiency of siHSP47 into EXOs, achieving 31.1% efficiency rate. EXOs were isolated from human embryonic kidney cells (293F) and loaded with siHSP47 (EXO-siHSP47). The findings indicated that EXO-siHSP47 penetrated the collagen barrier and effectively silenced HSP47 expression in activated fibroblasts in vitro. Western blotting and immunofluorescence analyses confirmed that EXO-siHSP47 significantly reduced the secretion and deposition of extracellular matrix (ECM) proteins. Wound healing and Transwell migration assays demonstrated that EXO-siHSP47 inhibited fibroblast differentiation and migration. In conclusion, 293F-derived EXOs loaded with siHSP47 present a promising therapeutic strategy for IPF.

PMID:39796239 | DOI:10.3390/ijms26010382